Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations1000
Missing cells633
Missing cells (%)2.8%
Duplicate rows33
Duplicate rows (%)3.3%
Total size in memory179.8 KiB
Average record size in memory184.1 B

Variable types

Categorical9
Numeric11
Text3

Alerts

Dataset has 33 (3.3%) duplicate rowsDuplicates
DBP is highly overall correlated with SBPHigh correlation
Dolor is highly overall correlated with dolor_NRSHigh correlation
Error_Triaje is highly overall correlated with Grupo_De_ErrorHigh correlation
Grupo is highly overall correlated with RR and 1 other fieldsHigh correlation
Grupo_De_Error is highly overall correlated with Error_TriajeHigh correlation
KTAS_enfermera is highly overall correlated with KTAS_expertoHigh correlation
KTAS_experto is highly overall correlated with KTAS_enfermeraHigh correlation
RR is highly overall correlated with GrupoHigh correlation
SBP is highly overall correlated with DBPHigh correlation
Saturacion is highly overall correlated with GrupoHigh correlation
dolor_NRS is highly overall correlated with DolorHigh correlation
Estado_Mental is highly imbalanced (78.4%)Imbalance
Error_Triaje is highly imbalanced (53.4%)Imbalance
SBP has 17 (1.7%) missing valuesMissing
DBP has 21 (2.1%) missing valuesMissing
HR has 12 (1.2%) missing valuesMissing
RR has 15 (1.5%) missing valuesMissing
BT has 11 (1.1%) missing valuesMissing
Saturacion has 556 (55.6%) missing valuesMissing
Grupo_De_Error has 851 (85.1%) zerosZeros
Duracion_Estancia_Min has 15 (1.5%) zerosZeros

Reproduction

Analysis started2024-09-13 20:32:46.613516
Analysis finished2024-09-13 20:33:03.089585
Duration16.48 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Grupo
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
550 
2
450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 550
55.0%
2 450
45.0%

Length

2024-09-13T15:33:03.133526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T15:33:03.185282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 550
55.0%
2 450
45.0%

Most occurring characters

ValueCountFrequency (%)
1 550
55.0%
2 450
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 550
55.0%
2 450
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 550
55.0%
2 450
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 550
55.0%
2 450
45.0%

Sexo
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
514 
1
486 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 514
51.4%
1 486
48.6%

Length

2024-09-13T15:33:03.243442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T15:33:03.299005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 514
51.4%
1 486
48.6%

Most occurring characters

ValueCountFrequency (%)
2 514
51.4%
1 486
48.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 514
51.4%
1 486
48.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 514
51.4%
1 486
48.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 514
51.4%
1 486
48.6%

Edad
Real number (ℝ)

Distinct78
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.892
Minimum16
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:03.359398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q136
median56
Q370.25
95-th percentile82
Maximum94
Range78
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation19.826483
Coefficient of variation (CV)0.36789288
Kurtosis-1.1073081
Mean53.892
Median Absolute Deviation (MAD)16
Skewness-0.20869706
Sum53892
Variance393.08943
MonotonicityNot monotonic
2024-09-13T15:33:03.440762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 26
 
2.6%
68 24
 
2.4%
22 23
 
2.3%
79 23
 
2.3%
74 23
 
2.3%
75 22
 
2.2%
69 21
 
2.1%
57 21
 
2.1%
81 20
 
2.0%
72 20
 
2.0%
Other values (68) 777
77.7%
ValueCountFrequency (%)
16 8
 
0.8%
17 4
 
0.4%
18 8
 
0.8%
19 10
1.0%
20 10
1.0%
21 9
 
0.9%
22 23
2.3%
23 7
 
0.7%
24 18
1.8%
25 13
1.3%
ValueCountFrequency (%)
94 1
 
0.1%
93 1
 
0.1%
92 1
 
0.1%
90 1
 
0.1%
89 1
 
0.1%
88 8
0.8%
87 4
0.4%
86 8
0.8%
85 5
0.5%
84 5
0.5%

Modo_Llegada
Real number (ℝ)

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.811
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:03.503114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79618648
Coefficient of variation (CV)0.28323959
Kurtosis2.1607592
Mean2.811
Median Absolute Deviation (MAD)0
Skewness0.1129183
Sum2811
Variance0.63391291
MonotonicityNot monotonic
2024-09-13T15:33:03.562678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 588
58.8%
2 220
 
22.0%
4 122
 
12.2%
1 60
 
6.0%
6 7
 
0.7%
5 2
 
0.2%
7 1
 
0.1%
ValueCountFrequency (%)
1 60
 
6.0%
2 220
 
22.0%
3 588
58.8%
4 122
 
12.2%
5 2
 
0.2%
6 7
 
0.7%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
6 7
 
0.7%
5 2
 
0.2%
4 122
 
12.2%
3 588
58.8%
2 220
 
22.0%
1 60
 
6.0%

Lesion
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
800 
2
200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 800
80.0%
2 200
 
20.0%

Length

2024-09-13T15:33:03.628385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T15:33:03.677828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 800
80.0%
2 200
 
20.0%

Most occurring characters

ValueCountFrequency (%)
1 800
80.0%
2 200
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 800
80.0%
2 200
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 800
80.0%
2 200
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 800
80.0%
2 200
 
20.0%
Distinct347
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:03.844167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length37
Median length26
Mean length12.239
Min length2

Characters and Unicode

Total characters12239
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)21.4%

Sample

1st rowant. chest pain
2nd rowheadache
3rd rowant. chest pain
4th rowheadache
5th rowfever & chill
ValueCountFrequency (%)
pain 325
 
16.3%
chest 81
 
4.1%
abd 80
 
4.0%
dizziness 57
 
2.9%
53
 
2.7%
dyspnea 48
 
2.4%
weakness 38
 
1.9%
lt 38
 
1.9%
ant 37
 
1.9%
injury 36
 
1.8%
Other values (274) 1206
60.3%
2024-09-13T15:33:04.101009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1216
 
9.9%
e 1090
 
8.9%
i 1074
 
8.8%
n 1052
 
8.6%
1001
 
8.2%
s 664
 
5.4%
t 617
 
5.0%
p 530
 
4.3%
d 453
 
3.7%
r 449
 
3.7%
Other values (51) 4093
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12239
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1216
 
9.9%
e 1090
 
8.9%
i 1074
 
8.8%
n 1052
 
8.6%
1001
 
8.2%
s 664
 
5.4%
t 617
 
5.0%
p 530
 
4.3%
d 453
 
3.7%
r 449
 
3.7%
Other values (51) 4093
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12239
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1216
 
9.9%
e 1090
 
8.9%
i 1074
 
8.8%
n 1052
 
8.6%
1001
 
8.2%
s 664
 
5.4%
t 617
 
5.0%
p 530
 
4.3%
d 453
 
3.7%
r 449
 
3.7%
Other values (51) 4093
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12239
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1216
 
9.9%
e 1090
 
8.9%
i 1074
 
8.8%
n 1052
 
8.6%
1001
 
8.2%
s 664
 
5.4%
t 617
 
5.0%
p 530
 
4.3%
d 453
 
3.7%
r 449
 
3.7%
Other values (51) 4093
33.4%

Estado_Mental
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
936 
2
 
33
3
 
23
4
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 936
93.6%
2 33
 
3.3%
3 23
 
2.3%
4 8
 
0.8%

Length

2024-09-13T15:33:04.191044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T15:33:04.260228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 936
93.6%
2 33
 
3.3%
3 23
 
2.3%
4 8
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 936
93.6%
2 33
 
3.3%
3 23
 
2.3%
4 8
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 936
93.6%
2 33
 
3.3%
3 23
 
2.3%
4 8
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 936
93.6%
2 33
 
3.3%
3 23
 
2.3%
4 8
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 936
93.6%
2 33
 
3.3%
3 23
 
2.3%
4 8
 
0.8%

Dolor
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
562 
0
438 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 562
56.2%
0 438
43.8%

Length

2024-09-13T15:33:04.323252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T15:33:04.378648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 562
56.2%
0 438
43.8%

Most occurring characters

ValueCountFrequency (%)
1 562
56.2%
0 438
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 562
56.2%
0 438
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 562
56.2%
0 438
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 562
56.2%
0 438
43.8%

dolor_NRS
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
#BOÞ!
440 
3
209 
4
111 
5
109 
6
59 
Other values (6)
72 

Length

Max length5
Median length1
Mean length2.763
Min length1

Characters and Unicode

Total characters2763
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2
2nd row4
3rd row3
4th row4
5th row#BOÞ!

Common Values

ValueCountFrequency (%)
#BOÞ! 440
44.0%
3 209
20.9%
4 111
 
11.1%
5 109
 
10.9%
6 59
 
5.9%
2 31
 
3.1%
7 27
 
2.7%
8 8
 
0.8%
10 3
 
0.3%
1 2
 
0.2%

Length

2024-09-13T15:33:04.441852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boþ 440
44.0%
3 209
20.9%
4 111
 
11.1%
5 109
 
10.9%
6 59
 
5.9%
2 31
 
3.1%
7 27
 
2.7%
8 8
 
0.8%
10 3
 
0.3%
1 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
# 440
15.9%
B 440
15.9%
O 440
15.9%
Þ 440
15.9%
! 440
15.9%
3 209
7.6%
4 111
 
4.0%
5 109
 
3.9%
6 59
 
2.1%
2 31
 
1.1%
Other values (5) 44
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2763
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
# 440
15.9%
B 440
15.9%
O 440
15.9%
Þ 440
15.9%
! 440
15.9%
3 209
7.6%
4 111
 
4.0%
5 109
 
3.9%
6 59
 
2.1%
2 31
 
1.1%
Other values (5) 44
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2763
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
# 440
15.9%
B 440
15.9%
O 440
15.9%
Þ 440
15.9%
! 440
15.9%
3 209
7.6%
4 111
 
4.0%
5 109
 
3.9%
6 59
 
2.1%
2 31
 
1.1%
Other values (5) 44
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2763
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
# 440
15.9%
B 440
15.9%
O 440
15.9%
Þ 440
15.9%
! 440
15.9%
3 209
7.6%
4 111
 
4.0%
5 109
 
3.9%
6 59
 
2.1%
2 31
 
1.1%
Other values (5) 44
 
1.6%

SBP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct124
Distinct (%)12.6%
Missing17
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean133.48525
Minimum50
Maximum275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:04.524148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile100
Q1114
median130
Q3150
95-th percentile182
Maximum275
Range225
Interquartile range (IQR)36

Descriptive statistics

Standard deviation27.156136
Coefficient of variation (CV)0.20343923
Kurtosis1.0065791
Mean133.48525
Median Absolute Deviation (MAD)20
Skewness0.61089959
Sum131216
Variance737.45574
MonotonicityNot monotonic
2024-09-13T15:33:04.614209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 96
 
9.6%
110 85
 
8.5%
140 71
 
7.1%
100 68
 
6.8%
130 60
 
6.0%
160 35
 
3.5%
150 33
 
3.3%
132 12
 
1.2%
136 12
 
1.2%
180 11
 
1.1%
Other values (114) 500
50.0%
(Missing) 17
 
1.7%
ValueCountFrequency (%)
50 1
 
0.1%
60 2
 
0.2%
65 1
 
0.1%
70 4
 
0.4%
75 1
 
0.1%
80 7
0.7%
86 3
 
0.3%
90 11
1.1%
91 3
 
0.3%
93 1
 
0.1%
ValueCountFrequency (%)
275 1
0.1%
233 1
0.1%
221 1
0.1%
220 1
0.1%
214 1
0.1%
213 1
0.1%
211 1
0.1%
210 2
0.2%
206 1
0.1%
205 2
0.2%

DBP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct78
Distinct (%)8.0%
Missing21
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean79.566905
Minimum31
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:04.683928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile60
Q170
median80
Q390
95-th percentile101
Maximum160
Range129
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.174593
Coefficient of variation (CV)0.19071488
Kurtosis1.0347037
Mean79.566905
Median Absolute Deviation (MAD)10
Skewness0.36899589
Sum77896
Variance230.26827
MonotonicityNot monotonic
2024-09-13T15:33:04.766871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 134
 
13.4%
70 108
 
10.8%
60 93
 
9.3%
100 79
 
7.9%
90 73
 
7.3%
76 20
 
2.0%
77 20
 
2.0%
71 16
 
1.6%
72 16
 
1.6%
78 14
 
1.4%
Other values (68) 406
40.6%
(Missing) 21
 
2.1%
ValueCountFrequency (%)
31 1
 
0.1%
33 1
 
0.1%
36 1
 
0.1%
40 3
0.3%
44 1
 
0.1%
45 4
0.4%
47 1
 
0.1%
50 6
0.6%
52 1
 
0.1%
53 2
 
0.2%
ValueCountFrequency (%)
160 1
0.1%
154 1
0.1%
136 1
0.1%
131 1
0.1%
123 1
0.1%
122 1
0.1%
120 1
0.1%
117 1
0.1%
115 2
0.2%
114 1
0.1%

HR
Real number (ℝ)

MISSING 

Distinct87
Distinct (%)8.8%
Missing12
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean84.40081
Minimum32
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:04.849659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile60
Q173
median82
Q396
95-th percentile111
Maximum148
Range116
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.297428
Coefficient of variation (CV)0.19309564
Kurtosis0.16194673
Mean84.40081
Median Absolute Deviation (MAD)11
Skewness0.36895414
Sum83388
Variance265.60616
MonotonicityNot monotonic
2024-09-13T15:33:04.925924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 74
 
7.4%
100 43
 
4.3%
78 37
 
3.7%
76 35
 
3.5%
88 34
 
3.4%
84 34
 
3.4%
68 31
 
3.1%
74 30
 
3.0%
72 30
 
3.0%
90 29
 
2.9%
Other values (77) 611
61.1%
ValueCountFrequency (%)
32 1
 
0.1%
35 1
 
0.1%
37 1
 
0.1%
48 1
 
0.1%
50 3
0.3%
51 2
0.2%
52 1
 
0.1%
53 2
0.2%
54 4
0.4%
56 4
0.4%
ValueCountFrequency (%)
148 1
0.1%
140 1
0.1%
137 2
0.2%
135 1
0.1%
131 1
0.1%
130 1
0.1%
128 2
0.2%
127 1
0.1%
126 2
0.2%
125 1
0.1%

RR
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)1.0%
Missing15
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean19.450761
Minimum14
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:04.995754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile16
Q118
median20
Q320
95-th percentile22
Maximum30
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0698823
Coefficient of variation (CV)0.10641652
Kurtosis4.5644118
Mean19.450761
Median Absolute Deviation (MAD)0
Skewness0.94443693
Sum19159
Variance4.2844125
MonotonicityNot monotonic
2024-09-13T15:33:05.058208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20 615
61.5%
18 151
 
15.1%
16 137
 
13.7%
22 39
 
3.9%
24 23
 
2.3%
28 11
 
1.1%
26 4
 
0.4%
30 3
 
0.3%
23 1
 
0.1%
14 1
 
0.1%
(Missing) 15
 
1.5%
ValueCountFrequency (%)
14 1
 
0.1%
16 137
 
13.7%
18 151
 
15.1%
20 615
61.5%
22 39
 
3.9%
23 1
 
0.1%
24 23
 
2.3%
26 4
 
0.4%
28 11
 
1.1%
30 3
 
0.3%
ValueCountFrequency (%)
30 3
 
0.3%
28 11
 
1.1%
26 4
 
0.4%
24 23
 
2.3%
23 1
 
0.1%
22 39
 
3.9%
20 615
61.5%
18 151
 
15.1%
16 137
 
13.7%
14 1
 
0.1%

BT
Real number (ℝ)

MISSING 

Distinct37
Distinct (%)3.7%
Missing11
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean36.562993
Minimum35
Maximum39.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:05.135614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile36
Q136.2
median36.5
Q336.8
95-th percentile37.5
Maximum39.8
Range4.8
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.51969855
Coefficient of variation (CV)0.014213786
Kurtosis5.810235
Mean36.562993
Median Absolute Deviation (MAD)0.3
Skewness1.7543191
Sum36160.8
Variance0.27008658
MonotonicityNot monotonic
2024-09-13T15:33:05.203997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
36 153
15.3%
36.5 122
12.2%
36.4 104
10.4%
36.6 96
9.6%
36.3 79
7.9%
36.2 78
7.8%
36.8 65
6.5%
36.7 52
 
5.2%
36.9 40
 
4.0%
37 38
 
3.8%
Other values (27) 162
16.2%
ValueCountFrequency (%)
35 1
 
0.1%
35.5 2
 
0.2%
35.6 2
 
0.2%
35.7 4
 
0.4%
35.8 1
 
0.1%
35.9 1
 
0.1%
36 153
15.3%
36.1 27
 
2.7%
36.2 78
7.8%
36.3 79
7.9%
ValueCountFrequency (%)
39.8 1
 
0.1%
39.5 2
 
0.2%
39.4 1
 
0.1%
39 1
 
0.1%
38.7 2
 
0.2%
38.5 2
 
0.2%
38.4 4
0.4%
38.3 1
 
0.1%
38.2 1
 
0.1%
38.1 6
0.6%

Saturacion
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)4.3%
Missing556
Missing (%)55.6%
Infinite0
Infinite (%)0.0%
Mean97.047297
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:05.287443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile93
Q197
median98
Q399
95-th percentile99
Maximum100
Range80
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.7611119
Coefficient of variation (CV)0.049059707
Kurtosis160.17907
Mean97.047297
Median Absolute Deviation (MAD)1
Skewness-10.8945
Sum43089
Variance22.668187
MonotonicityNot monotonic
2024-09-13T15:33:05.349084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
98 176
 
17.6%
99 93
 
9.3%
97 71
 
7.1%
96 36
 
3.6%
100 19
 
1.9%
95 15
 
1.5%
94 10
 
1.0%
90 6
 
0.6%
93 3
 
0.3%
92 3
 
0.3%
Other values (9) 12
 
1.2%
(Missing) 556
55.6%
ValueCountFrequency (%)
20 1
 
0.1%
68 1
 
0.1%
74 1
 
0.1%
76 1
 
0.1%
78 1
 
0.1%
85 1
 
0.1%
86 2
 
0.2%
88 2
 
0.2%
90 6
0.6%
91 2
 
0.2%
ValueCountFrequency (%)
100 19
 
1.9%
99 93
9.3%
98 176
17.6%
97 71
7.1%
96 36
 
3.6%
95 15
 
1.5%
94 10
 
1.0%
93 3
 
0.3%
92 3
 
0.3%
91 2
 
0.2%

KTAS_enfermera
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
4
400 
3
363 
2
166 
5
59 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 400
40.0%
3 363
36.3%
2 166
16.6%
5 59
 
5.9%
1 12
 
1.2%

Length

2024-09-13T15:33:05.425388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T15:33:05.488486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 400
40.0%
3 363
36.3%
2 166
16.6%
5 59
 
5.9%
1 12
 
1.2%

Most occurring characters

ValueCountFrequency (%)
4 400
40.0%
3 363
36.3%
2 166
16.6%
5 59
 
5.9%
1 12
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 400
40.0%
3 363
36.3%
2 166
16.6%
5 59
 
5.9%
1 12
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 400
40.0%
3 363
36.3%
2 166
16.6%
5 59
 
5.9%
1 12
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 400
40.0%
3 363
36.3%
2 166
16.6%
5 59
 
5.9%
1 12
 
1.2%
Distinct489
Distinct (%)48.9%
Missing1
Missing (%)0.1%
Memory size7.9 KiB
2024-09-13T15:33:05.680864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length99
Median length61
Mean length24.265265
Min length4

Characters and Unicode

Total characters24241
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique323 ?
Unique (%)32.3%

Sample

1st rowIschaemic chest pain
2nd rowSubarachnoid haemorrhage, unspecified
3rd rowContusion of front wall of thorax
4th rowHeadache
5th rowFever
ValueCountFrequency (%)
of 224
 
7.1%
unspecified 135
 
4.3%
acute 99
 
3.2%
pain 88
 
2.8%
and 58
 
1.8%
other 42
 
1.3%
gastroenteritis 41
 
1.3%
with 37
 
1.2%
nos 35
 
1.1%
chest 34
 
1.1%
Other values (624) 2349
74.8%
2024-09-13T15:33:05.961891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2317
 
9.6%
2143
 
8.8%
i 2083
 
8.6%
a 1870
 
7.7%
n 1565
 
6.5%
r 1499
 
6.2%
t 1490
 
6.1%
o 1475
 
6.1%
s 1351
 
5.6%
c 1090
 
4.5%
Other values (58) 7358
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2317
 
9.6%
2143
 
8.8%
i 2083
 
8.6%
a 1870
 
7.7%
n 1565
 
6.5%
r 1499
 
6.2%
t 1490
 
6.1%
o 1475
 
6.1%
s 1351
 
5.6%
c 1090
 
4.5%
Other values (58) 7358
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2317
 
9.6%
2143
 
8.8%
i 2083
 
8.6%
a 1870
 
7.7%
n 1565
 
6.5%
r 1499
 
6.2%
t 1490
 
6.1%
o 1475
 
6.1%
s 1351
 
5.6%
c 1090
 
4.5%
Other values (58) 7358
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2317
 
9.6%
2143
 
8.8%
i 2083
 
8.6%
a 1870
 
7.7%
n 1565
 
6.5%
r 1499
 
6.2%
t 1490
 
6.1%
o 1475
 
6.1%
s 1351
 
5.6%
c 1090
 
4.5%
Other values (58) 7358
30.4%

Disposicion
Real number (ℝ)

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.61
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:06.036376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1615719
Coefficient of variation (CV)0.72147322
Kurtosis8.668019
Mean1.61
Median Absolute Deviation (MAD)0
Skewness2.848138
Sum1610
Variance1.3492492
MonotonicityNot monotonic
2024-09-13T15:33:06.095771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 633
63.3%
2 288
28.8%
5 28
 
2.8%
4 22
 
2.2%
7 17
 
1.7%
6 6
 
0.6%
3 6
 
0.6%
ValueCountFrequency (%)
1 633
63.3%
2 288
28.8%
3 6
 
0.6%
4 22
 
2.2%
5 28
 
2.8%
6 6
 
0.6%
7 17
 
1.7%
ValueCountFrequency (%)
7 17
 
1.7%
6 6
 
0.6%
5 28
 
2.8%
4 22
 
2.2%
3 6
 
0.6%
2 288
28.8%
1 633
63.3%

KTAS_experto
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
391 
4
369 
2
171 
5
50 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 391
39.1%
4 369
36.9%
2 171
17.1%
5 50
 
5.0%
1 19
 
1.9%

Length

2024-09-13T15:33:06.163059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T15:33:06.221758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 391
39.1%
4 369
36.9%
2 171
17.1%
5 50
 
5.0%
1 19
 
1.9%

Most occurring characters

ValueCountFrequency (%)
3 391
39.1%
4 369
36.9%
2 171
17.1%
5 50
 
5.0%
1 19
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 391
39.1%
4 369
36.9%
2 171
17.1%
5 50
 
5.0%
1 19
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 391
39.1%
4 369
36.9%
2 171
17.1%
5 50
 
5.0%
1 19
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 391
39.1%
4 369
36.9%
2 171
17.1%
5 50
 
5.0%
1 19
 
1.9%

Grupo_De_Error
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.569
Minimum0
Maximum9
Zeros851
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:06.279150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5873748
Coefficient of variation (CV)2.7897624
Kurtosis9.9224709
Mean0.569
Median Absolute Deviation (MAD)0
Skewness3.1430416
Sum569
Variance2.5197588
MonotonicityNot monotonic
2024-09-13T15:33:06.334146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 851
85.1%
4 50
 
5.0%
2 39
 
3.9%
1 16
 
1.6%
5 11
 
1.1%
8 9
 
0.9%
7 7
 
0.7%
6 6
 
0.6%
3 6
 
0.6%
9 5
 
0.5%
ValueCountFrequency (%)
0 851
85.1%
1 16
 
1.6%
2 39
 
3.9%
3 6
 
0.6%
4 50
 
5.0%
5 11
 
1.1%
6 6
 
0.6%
7 7
 
0.7%
8 9
 
0.9%
9 5
 
0.5%
ValueCountFrequency (%)
9 5
 
0.5%
8 9
 
0.9%
7 7
 
0.7%
6 6
 
0.6%
5 11
 
1.1%
4 50
 
5.0%
3 6
 
0.6%
2 39
 
3.9%
1 16
 
1.6%
0 851
85.1%

Duracion_Estancia_Min
Real number (ℝ)

ZEROS 

Distinct618
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12792.48
Minimum0
Maximum709510
Zeros15
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:06.402185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.95
Q1132.75
median270.5
Q3620
95-th percentile10100.9
Maximum709510
Range709510
Interquartile range (IQR)487.25

Descriptive statistics

Standard deviation87721.151
Coefficient of variation (CV)6.8572436
Kurtosis57.628865
Mean12792.48
Median Absolute Deviation (MAD)178.5
Skewness7.7083186
Sum12792480
Variance7.6950004 × 109
MonotonicityNot monotonic
2024-09-13T15:33:06.480180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
1.5%
129 7
 
0.7%
46 7
 
0.7%
182 7
 
0.7%
183 6
 
0.6%
83 6
 
0.6%
172 6
 
0.6%
161 5
 
0.5%
96 5
 
0.5%
174 5
 
0.5%
Other values (608) 931
93.1%
ValueCountFrequency (%)
0 15
1.5%
1 2
 
0.2%
4 1
 
0.1%
12 1
 
0.1%
16 2
 
0.2%
19 1
 
0.1%
20 2
 
0.2%
21 4
 
0.4%
22 1
 
0.1%
23 4
 
0.4%
ValueCountFrequency (%)
709510 1
0.1%
700875 1
0.1%
700427 1
0.1%
700092 1
0.1%
699878 1
0.1%
699861 1
0.1%
699667 1
0.1%
699303 1
0.1%
699091 1
0.1%
698919 1
0.1%
Distinct335
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-09-13T15:33:06.702211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.116
Min length4

Characters and Unicode

Total characters4116
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)20.4%

Sample

1st row2,00
2nd row3,00
3rd row2,00
4th row2,00
5th row3,60
ValueCountFrequency (%)
2,00 133
 
13.3%
3,00 117
 
11.7%
4,00 112
 
11.2%
5,00 65
 
6.5%
1,00 30
 
3.0%
6,00 6
 
0.6%
5,38 6
 
0.6%
5,65 5
 
0.5%
4,15 5
 
0.5%
8,17 5
 
0.5%
Other values (325) 516
51.6%
2024-09-13T15:33:06.994577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1093
26.6%
, 1000
24.3%
2 326
 
7.9%
3 320
 
7.8%
5 286
 
6.9%
4 228
 
5.5%
1 222
 
5.4%
8 202
 
4.9%
7 201
 
4.9%
6 146
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1093
26.6%
, 1000
24.3%
2 326
 
7.9%
3 320
 
7.8%
5 286
 
6.9%
4 228
 
5.5%
1 222
 
5.4%
8 202
 
4.9%
7 201
 
4.9%
6 146
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1093
26.6%
, 1000
24.3%
2 326
 
7.9%
3 320
 
7.8%
5 286
 
6.9%
4 228
 
5.5%
1 222
 
5.4%
8 202
 
4.9%
7 201
 
4.9%
6 146
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1093
26.6%
, 1000
24.3%
2 326
 
7.9%
3 320
 
7.8%
5 286
 
6.9%
4 228
 
5.5%
1 222
 
5.4%
8 202
 
4.9%
7 201
 
4.9%
6 146
 
3.5%

Error_Triaje
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
851 
2
104 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 851
85.1%
2 104
 
10.4%
1 45
 
4.5%

Length

2024-09-13T15:33:07.312821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-13T15:33:07.364623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 851
85.1%
2 104
 
10.4%
1 45
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 851
85.1%
2 104
 
10.4%
1 45
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 851
85.1%
2 104
 
10.4%
1 45
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 851
85.1%
2 104
 
10.4%
1 45
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 851
85.1%
2 104
 
10.4%
1 45
 
4.5%

Interactions

2024-09-13T15:33:01.707867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:48.276756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.247042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.048603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.812015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:55.977920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:57.154699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:58.896982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.600510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.278366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.979167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.776705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:48.931785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.346655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.111301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.888350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:56.130656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:57.329018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:58.965332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.658467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.347491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.048016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.842152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:50.008443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.439471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.181198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.964994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:56.264088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:57.454940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.029135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.722975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.410178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.113790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.906462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:51.399659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.504551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.243236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:55.041449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:56.350945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:57.531144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.095287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.785759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.472492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.166366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.973922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:52.165609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.562682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.314130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:55.205737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:56.454607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:57.669558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.162397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.848416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.537157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.228379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:02.063757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:52.375265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.641330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.382085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:55.304172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:56.537422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:57.800766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.226673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.903873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.597371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.290863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:02.152170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:52.617419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.716078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.451275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:55.408202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:56.628146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:57.901251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.293069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.973097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.665933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.402073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:02.225708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:52.745484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.785170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.514078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:55.506612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:56.726011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:57.968369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.351007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.037534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.729004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.462704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:02.306631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:52.874842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.849052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.584020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:55.632282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:56.810029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:58.687112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.414109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.101162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.787185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.523198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:02.380686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:52.995501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.909866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.668334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:55.735318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:56.900537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:58.751617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.473584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.160083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.853828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.582522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:02.448002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.095384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:53.972709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:54.728963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:55.846431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:57.015976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:58.824572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:32:59.535659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.217770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:00.910014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-13T15:33:01.643000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-13T15:33:07.416461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BTDBPDisposicionDolorDuracion_Estancia_MinEdadError_TriajeEstado_MentalGrupoGrupo_De_ErrorHRKTAS_enfermeraKTAS_expertoLesionModo_LlegadaRRSBPSaturacionSexodolor_NRS
BT1.0000.003-0.0110.017-0.047-0.0660.0950.1540.3290.0260.1670.0850.0960.059-0.015-0.0960.083-0.0530.0000.034
DBP0.0031.000-0.0800.111-0.1090.0000.0500.1270.182-0.0280.0660.1010.1190.131-0.0940.0080.7540.0540.0780.000
Disposicion-0.011-0.0801.0000.1450.4360.2500.0720.3020.3460.0650.0670.3010.3180.1590.1490.187-0.150-0.1200.0450.024
Dolor0.0170.1110.1451.0000.0250.1500.0220.2230.0400.2110.0650.2610.2580.1840.1610.1350.1200.1520.0000.991
Duracion_Estancia_Min-0.047-0.1090.4360.0251.0000.2490.0000.0000.1290.0600.0420.0350.0150.0000.1500.322-0.217-0.0760.0000.000
Edad-0.0660.0000.2500.1500.2491.0000.0330.0000.1500.049-0.0650.1240.1210.2200.0090.0850.066-0.2280.1110.021
Error_Triaje0.0950.0500.0720.0220.0000.0331.0000.0740.0750.7320.0570.1650.2080.0690.0550.0340.0370.0730.0000.152
Estado_Mental0.1540.1270.3020.2230.0000.0000.0741.0000.0210.1560.1100.4880.4140.0000.1700.2300.1370.2500.0560.095
Grupo0.3290.1820.3460.0400.1290.1500.0750.0211.0000.0850.0970.1640.2020.0360.3430.5170.3151.0000.0000.218
Grupo_De_Error0.026-0.0280.0650.2110.0600.0490.7320.1560.0851.0000.0510.0890.1890.0000.0350.082-0.0660.0170.0000.095
HR0.1670.0660.0670.0650.042-0.0650.0570.1100.0970.0511.0000.0870.1210.0510.0190.0790.031-0.0460.0280.000
KTAS_enfermera0.0850.1010.3010.2610.0350.1240.1650.4880.1640.0890.0871.0000.7530.2850.1740.1200.1260.1050.0790.211
KTAS_experto0.0960.1190.3180.2580.0150.1210.2080.4140.2020.1890.1210.7531.0000.3180.1850.1720.2310.2630.0910.228
Lesion0.0590.1310.1590.1840.0000.2200.0690.0000.0360.0000.0510.2850.3181.0000.0500.0810.0000.0000.0830.276
Modo_Llegada-0.015-0.0940.1490.1610.1500.0090.0550.1700.3430.0350.0190.1740.1850.0501.0000.187-0.124-0.1320.0870.096
RR-0.0960.0080.1870.1350.3220.0850.0340.2300.5170.0820.0790.1200.1720.0810.1871.000-0.116-0.0990.0000.101
SBP0.0830.754-0.1500.120-0.2170.0660.0370.1370.315-0.0660.0310.1260.2310.000-0.124-0.1161.0000.0570.0380.011
Saturacion-0.0530.054-0.1200.152-0.076-0.2280.0730.2501.0000.017-0.0460.1050.2630.000-0.132-0.0990.0571.0000.0150.000
Sexo0.0000.0780.0450.0000.0000.1110.0000.0560.0000.0000.0280.0790.0910.0830.0870.0000.0380.0151.0000.000
dolor_NRS0.0340.0000.0240.9910.0000.0210.1520.0950.2180.0950.0000.2110.2280.2760.0960.1010.0110.0000.0001.000

Missing values

2024-09-13T15:33:02.553737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-13T15:33:02.924354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-13T15:33:03.032364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GrupoSexoEdadModo_LlegadaLesionQueja_PrincipalEstado_MentalDolordolor_NRSSBPDBPHRRRBTSaturacionKTAS_enfermeraDiagnostico_En_UrgenciasDisposicionKTAS_expertoGrupo_De_ErrorDuracion_Estancia_MinDuracion_KTAS_MinError_Triaje
0224921ant. chest pain112150.090.092.020.036.298.02Ischaemic chest pain22015912,000
1223041headache114140.080.076.020.036.399.03Subarachnoid haemorrhage, unspecified7302113,000
2216132ant. chest pain113100.060.084.020.036.498.04Contusion of front wall of thorax1401192,000
3226131headache114120.070.076.020.036.599.04Headache1344142,002
4126731fever & chill10#BOÞ!143.070.0130.020.038.1NaN4Fever1212673,602
5227731dyspnea113110.070.065.020.036.595.04Heart failure, unspecified240102544,000
6117431abd pain116118.078.077.016.036.9NaN3Parkinsonism130568,720
7215842post-CPR state40#BOÞ!140.080.0122.0NaN36.090.01Cardiac arrest with successful resuscitation21010102,000
8225821ant. chest pain114140.090.060.020.036.099.02Ischaemic chest pain22096192,000
9222721LLQ pain113130.080.092.020.036.597.04Unspecified abdominal pain1404932,000
GrupoSexoEdadModo_LlegadaLesionQueja_PrincipalEstado_MentalDolordolor_NRSSBPDBPHRRRBTSaturacionKTAS_enfermeraDiagnostico_En_UrgenciasDisposicionKTAS_expertoGrupo_De_ErrorDuracion_Estancia_MinDuracion_KTAS_MinError_Triaje
990117522altered mentality30#BOÞ!NaNNaNNaNNaNNaNNaN2Subarachnoid hemorrhage, traumatic without open wound2205046,320
991122822eyebrow laceration213148.092.0106.020.036.7NaN4Laceration of face1402073,220
992123421abd pain114154.095.098.018.037.2NaN3Acute gastroenteritis1301504,750
993117431abd pain113189.0100.079.018.036.3NaN4Symptomatic gallbladder stone without obstruction2405614,820
994217941right hemiparesis10#BOÞ!160.0100.064.020.036.594.03Intracerebral haemorrhage in basal ganglia2282443,002
995127341fever10#BOÞ!117.060.085.016.036.8NaN4Fever2406209,750
996112731Suicidal Attempt10#BOÞ!110.069.096.020.036.2NaN3Suicide tendency2304427,420
997125421pain, chest116159.089.091.020.036.6NaN2Gastroesophageal reflux disease1202144,950
998111931eye discomfort10#BOÞ!132.078.091.020.036.5NaN4Corneal erosion140503,450
999122931pain, abdominal116137.092.058.018.037.6NaN3Allergic reaction1301165,970

Duplicate rows

Most frequently occurring

GrupoSexoEdadModo_LlegadaLesionQueja_PrincipalEstado_MentalDolordolor_NRSSBPDBPHRRRBTSaturacionKTAS_enfermeraDiagnostico_En_UrgenciasDisposicionKTAS_expertoGrupo_De_ErrorDuracion_Estancia_MinDuracion_KTAS_MinError_Triaje# duplicates
0111931eye discomfort10#BOÞ!132.078.091.020.036.5NaN4Corneal erosion140503,4502
1112222headache115106.069.071.016.036.5NaN3Multiple abrasion1301486,8702
2112731Suicidal Attempt10#BOÞ!110.069.096.020.036.2NaN3Suicide tendency2304427,4202
3112831Facial Palsy10#BOÞ!134.076.068.020.037.0NaN3Bell's palsy130753,0702
4114631pain, chest114153.067.067.018.036.5NaN3Gastroesophageal reflux disease130966,9802
5115031???10#BOÞ!140.065.060.016.037.0NaN3Depression23017213,6802
6115731Discomfort, Chest114151.077.079.020.036.9NaN3Depression1301636,3502
7116131????11390.053.0109.016.037.0NaN5Rash2502916,9202
8117431abd pain113189.0100.079.018.036.3NaN4Symptomatic gallbladder stone without obstruction2405614,8202
9117522altered mentality30#BOÞ!NaNNaNNaNNaNNaNNaN2Subarachnoid hemorrhage, traumatic without open wound2205046,3202